Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning.
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| Title: | Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning. |
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| Authors: | Valmaggia P; Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland.; Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland.; Department of Ophthalmology, University Hospital Basel, Basel, Switzerland., Friedli P; Supercomputing Systems AG, Zürich, Switzerland., Hörmann B; Supercomputing Systems AG, Zürich, Switzerland., Kaiser P; Supercomputing Systems AG, Zürich, Switzerland., Scholl HPN; Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland.; Department of Ophthalmology, University Hospital Basel, Basel, Switzerland., Cattin PC; Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland., Sandkühler R; Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland., Maloca PM; Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland.; Department of Ophthalmology, University Hospital Basel, Basel, Switzerland.; Moorfields Eye Hospital NHS Foundation Trust, London, EC1V 2PD, UK. |
| Source: | Translational vision science & technology [Transl Vis Sci Technol] 2022 Sep 01; Vol. 11 (9), pp. 25. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Association for Research in Vision and Ophthalmology Country of Publication: United States NLM ID: 101595919 Publication Model: Print Cited Medium: Internet ISSN: 2164-2591 (Electronic) Linking ISSN: 21642591 NLM ISO Abbreviation: Transl Vis Sci Technol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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